To see the other types of publications on this topic, follow the link: Image Sharpening Algorithm.
Journal articles on the topic 'Image Sharpening Algorithm'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Image Sharpening Algorithm.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Rokni, Komeil. "INVESTIGATING THE IMPACT OF PAN SHARPENING ON THE ACCURACY OF LAND COVER MAPPING IN LANDSAT OLI IMAGERY." Geodesy and cartography 49, no. 1 (2023): 12–18. http://dx.doi.org/10.3846/gac.2023.15308.
Full textAbstract:
Pan Sharpening is normally applied to sharpen a multispectral image with low resolution by using a panchromatic image with a higher resolution, to generate a high resolution multispectral image. The present study aims at assessing the power of Pan Sharpening on improvement of the accuracy of image classification and land cover mapping in Landsat 8 OLI imagery. In this respect, different Pan Sharpening algorithms including Brovey, Gram-Schmidt, NNDiffuse, and Principal Components were applied to merge the Landsat OLI panchromatic band (15 m) with the Landsat OLI multispectral: visible and infrared bands (30 m), to generate a new multispectral image with a higher spatial resolution (15 m). Subsequently, the support vector machine approach was utilized to classify the original Landsat and resulting Pan Sharpened images to generate land cover maps of the study area. The outcomes were then compared through the generation of confusion matrix and calculation of kappa coefficient and overall accuracy. The results indicated superiority of NNDiffuse algorithm in Pan Sharpening and improvement of classification accuracy in Landsat OLI imagery, with an overall accuracy and kappa coefficient of about 98.66% and 0.98, respectively. Furthermore, the result showed that the Gram-Schmidt and Principal Components algorithms also slightly improved the accuracy of image classification compared to original Landsat image. The study concluded that image Pan Sharpening is useful to improve the accuracy of image classification in Landsat OLI imagery, depending on the Pan Sharpening algorithm used for this purpose.
2
Tasaki, Kuniharu, Tomohisa Nishimura, Taro Hida, Kazushi Maruo, and Tetsuro Oshika. "Effects of Image Processing Using Honeycomb-Removal and Image-Sharpening Algorithms on Visibility of 27-Gauge Endoscopic Vitrectomy." Journal of Clinical Medicine 11, no. 19 (2022): 5666. http://dx.doi.org/10.3390/jcm11195666.
Full textAbstract:
Endoscopic vitrectomy with small gauge probes has clinical potentials, but intraocular visibility is inherently limited by low resolution and dim illumination due to the reduced number of optic fibers. We investigated whether honeycomb-removal and image-sharpening algorithms, which enable real-time processing of live images with a delay of 0.004 s, can improve the visibility of 27-gauge endoscopic vitrectomy. A total of 33 images during endoscopic vitrectomy were prepared, consisting of 11 original images, 11 images after the honeycomb-removal process, and 11 images after both honeycomb-removal and image-sharpening procedures. They were randomly presented to 18 vitreous surgeons, who rated each image on a 10-point scale. The honeycomb-removal algorithm almost completely suppressed honeycomb artifacts without degrading the background image quality. The implementation of image-sharpening algorithms further improved endoscopic visibility by optimizing contrast and augmenting image clarity. The visibility score was significantly improved from 4.27 ± 1.78 for the original images to 4.72 ± 2.00 for the images after the honeycomb-removal process (p < 0.001, linear mixed effects model), and to 5.40 ± 2.10 for the images after both the honeycomb-removal and image-sharpening procedures (p < 0.001). When the visibility scores were analyzed separately for 10 surgeons who were familiar with endoscopic vitrectomy and 8 surgeons who were not, similar results were obtained. Image processing with honeycomb-removal and image-sharpening algorithms significantly improved the visibility of 27-gauge endoscopic vitrectomy.
3
Beene, Daniel, Su Zhang, Christopher D. Lippitt, and Susan M. Bogus. "Performance Evaluation of Multiple Pan-Sharpening Techniques on NDVI: A Statistical Framework." Geographies 2, no. 3 (2022): 435–52. http://dx.doi.org/10.3390/geographies2030027.
Full textAbstract:
Pan-sharpening is a pixel-level image fusion process whereby a lower-spatial-resolution multispectral image is merged with a higher-spatial-resolution panchromatic one. One of the drawbacks of this process is that it may introduce spectral or radiometric distortion. The degree to which distortion is introduced is dependent on the imaging sensor, the pan-sharpening algorithm employed, and the context of the scene analyzed. Studies that evaluate the quality of pan-sharpening algorithms often fail to account for changes in geographic context and are agnostic to any specific applications of an end user. This research proposes an evaluation framework to assess the effects of six widely used pan-sharpening algorithms on normalized difference vegetation index (NDVI) calculation in five contextually diverse geographic locations. Output image quality is assessed by comparing the empirical cumulative density function of NDVI values that are calculated by using pre-sharpened and sharpened imagery. The premise is that an effective algorithm will generate a sharpened multispectral image with a cumulative NDVI distribution that is similar to the pre-sharpened image. Research results revealed that, generally, the Gram–Schmidt algorithm introduces a significant degree of spectral distortion regardless of sensor and spatial context. In addition, higher-spatial-resolution imagery is more susceptible to spectral distortions upon pan-sharpening. Furthermore, variability in cumulative density of spectral information in fused images justifies the application of an analytical framework to assist users in selecting the most effective methods for their intended application.
4
Nasonov, A., A. Krylov, and D. Lyukov. "IMAGE SHARPENING WITH BLUR MAP ESTIMATION USING CONVOLUTIONAL NEURAL NETWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 161–66. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-161-2019.
Full textAbstract:
<p><strong>Abstract.</strong> We propose a method for choosing optimal values of the parameters of image sharpening algorithm for out-of-focus blur based on grid warping approach. The idea of the considered sharpening algorithm is to move pixels from the edge neighborhood towards the edge centerlines. Compared to traditional deblurring algorithms, this approach requires only scalar blur level value rather than a blur kernel. We propose a convolutional neural network based algorithm for estimating the blur level value.</p>
5
Qiu, Yi Min, Shi Hong Chen, Yi Zhou, and Xin Hai Liu. "Stereoscopic Images Enhancement Based on Edge Sharpening of Wavelet Coefficients." Applied Mechanics and Materials 511-512 (February 2014): 490–94. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.490.
Full textAbstract:
This paper proposed a new image enhancement algorithm based on edge sharpening of wavelet coefficients for stereoscopic images. Our scheme uses the multi-scale characteristic of wavelet transform, decomposes the original image into low frequency approximation sub-graph and several high frequency direction. Under the multi-scale, the low frequency approximation sub-graph is processed by edge sharpening method. Then the low frequency sub-graph decomposes in multi-scale again. At last, the low frequency approximation graph after four layers decompose sharpening and the high frequency approximation of the decomposed sub-graph will be refactored to get the new image. Experimental results show that whether PSNR or visual effect, or the subjective assessment of the DMOS value, the proposed method has better enhanced performance than the conventional edge sharpening and wavelet transform. And it has good image edge enhancement, details protection. Meanwhile, the proposed algorithm has the same computational complexity with wavelet transform.
6
Luo, Sheng Min. "A Image Enhancement Algorithm Combined Wavelet Transform with Image Fusion." Applied Mechanics and Materials 182-183 (June 2012): 1832–38. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1832.
Full textAbstract:
Aiming at problems of poor contrast and blurred edges in degraded images, a novel enhancement algorithm is proposed in present research. The algorithm utilizes wavelet-based image fusion to accomplish the duplex enhancement task. Experiment results prove that the proposed enhancement algorithm can efficiently combine the merits of histogram equalization and sharpening, improving both the contrast and sharpness of the degraded image at the same time.
7
Modak, Sourav, Jonathan Heil, and Anthony Stein. "Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network." Remote Sensing 16, no. 5 (2024): 874. http://dx.doi.org/10.3390/rs16050874.
Full textAbstract:
Image preprocessing and fusion are commonly used for enhancing remote-sensing images, but the resulting images often lack useful spatial features. As the majority of research on image fusion has concentrated on the satellite domain, the image-fusion task for Unmanned Aerial Vehicle (UAV) images has received minimal attention. This study investigated an image-improvement strategy by integrating image preprocessing and fusion tasks for UAV images. The goal is to improve spatial details and avoid color distortion in fused images. Techniques such as image denoising, sharpening, and Contrast Limited Adaptive Histogram Equalization (CLAHE) were used in the preprocessing step. The unsharp mask algorithm was used for image sharpening. Wiener and total variation denoising methods were used for image denoising. The image-fusion process was conducted in two steps: (1) fusing the spectral bands into one multispectral image and (2) pansharpening the panchromatic and multispectral images using the PanColorGAN model. The effectiveness of the proposed approach was evaluated using quantitative and qualitative assessment techniques, including no-reference image quality assessment (NR-IQA) metrics. In this experiment, the unsharp mask algorithm noticeably improved the spatial details of the pansharpened images. No preprocessing algorithm dramatically improved the color quality of the enhanced images. The proposed fusion approach improved the images without importing unnecessary blurring and color distortion issues.
8
Chen, Xiaohua, Qiang Sheng, and Bhupesh Kumar Singh. "Aerobics Image Classification Algorithm Based on Modal Symmetry Algorithm." Computational Intelligence and Neuroscience 2021 (September 3, 2021): 1–9. http://dx.doi.org/10.1155/2021/5970957.
Full textAbstract:
There exist large numbers of methods/algorithms which can be used for the classification of aerobic images. While the current method is used to classify the aerobics image, it cannot effectively remove the noise in the aerobics image. The classification time is long, and there are problems of poor denoising effect and low classification efficiency. Therefore, the aerobics image classification algorithm based on the modal symmetry algorithm is proposed. The method of nonlocal mean filtering based on structural features is used to denoise the aerobics image, and the pyramid structure of the image is introduced to decompose the aerobics image. According to the denoising and decomposition results, the enhancement of aerobics image is realized by the logarithmic image processing (LIP) model and gradient sharpening method. Finally, the aerobics image after the enhancement is classified by a modal symmetry algorithm. Experimental results show that the proposed method has a good denoising effect and high classification efficiency, which shows that the algorithm has significant effectiveness and high application performance.
9
Qian Weixian, 钱惟贤, 陈钱 Chen Qian, 顾国华 Gu Guohua, and 管志强 Guan Zhiqiang. "Infrared Image Sharpening Algorithm With Noise Inhibition." Acta Optica Sinica 29, no. 7 (2009): 1807–11. http://dx.doi.org/10.3788/aos20092907.1807.
Full text
10
Zhu, Baoyu, Qunbo Lv, and Zheng Tan. "Adaptive Multi-Scale Fusion Blind Deblurred Generative Adversarial Network Method for Sharpening Image Data." Drones 7, no. 2 (2023): 96. http://dx.doi.org/10.3390/drones7020096.
Full textAbstract:
Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of blurred aerial remote sensing images. Moreover, the average PSNR of this paper’s algorithm tested on the publicly available dataset RealBlur-R reached 41.02 dB, surpassing the latest SOTA algorithm.
11
Patil, Prajakta, C.M. Sheela Rani, and Meenakshi Arya. "A hybrid pan-sharpening approach using maximum local extrema." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 255–63. https://doi.org/10.11591/ijece.v9i1.pp255-263.
Full textAbstract:
Mixing or combining different elements for getting enhanced version, is practiced across various areas in real life. Pan-sharpening is a similar technique used in the digital world; a process to combine two images into a fused image that comprises more detailed information. Images referred herein are Panchromatic (PAN) and Multispectral (MS) images. This paper presents a pansharpening algorithm which integrates multispectral and panchromatic images to generate an improved multispectral image. This technique merges the Discrete wavelet transform (WT) and Intensity-HueSaturation (IHS) through separate fusing criterion for choosing an approximate and detail sub-images. Whereas the maximal local extrema are used for merging detail sub-images and finally merged high-resolution image is reconstructed through inverse transform of wavelet and IHS. The proposed fusion approach enhances the superiority of the resultant fused image is demonstrated by quality measures like CORR, RMSE, PFE, SSIM, SNR and PSNR with the help of satellite Worldview-II images. The proposed algorithm is correlated with the other fusion techniques through empirical outcomes proves the superiority of the final merged image in terms of resolutions than the others.
12
Rahimzadeganasl, Alganci, and Goksel. "An Approach for the Pan Sharpening of Very High Resolution Satellite Images Using a CIELab Color Based Component Substitution Algorithm." Applied Sciences 9, no. 23 (2019): 5234. http://dx.doi.org/10.3390/app9235234.
Full textAbstract:
Recent very high spatial resolution (VHR) remote sensing satellites provide high spatial resolution panchromatic (Pan) images in addition to multispectral (MS) images. The pan sharpening process has a critical role in image processing tasks and geospatial information extraction from satellite images. In this research, CIELab color based component substitution Pan sharpening algorithm was proposed for Pan sharpening of the Pleiades VHR images. The proposed method was compared with the state-of-the-art Pan sharpening methods, such as IHS, EHLERS, NNDiffuse and GIHS. The selected study region included ten test sites, each of them representing complex landscapes with various land categories, to evaluate the performance of Pan sharpening methods in varying land surface characteristics. The spatial and spectral performance of the Pan sharpening methods were evaluated by eleven accuracy metrics and visual interpretation. The results of the evaluation indicated that proposed CIELab color-based method reached promising results and improved the spectral and spatial information preservation.
13
Zhu, Li, and Chun Qiang Zhu. "An Improved Adaptive Unsharp Masking Image Enhancement Algorithm." Applied Mechanics and Materials 325-326 (June 2013): 1547–50. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1547.
Full textAbstract:
When needing a reliable adaptive image contrast enhancement in real-time processing such as digital TV postprocessing,This goal is achieved by an improved adaptive unsharp masking image enhancement algorithm in the paper. The proposed improved adaptive unsharp masking filter controls the contribution of the sharpening path by the output of Laplacian and works in such a way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas. The experiment shows that this improved algorithm greatly reduce the complexity of computing and can be reliably used.
14
Patil, Prajakta, C. M. Sheela Rani, and Meenakshi Arya. "A hybrid pan-sharpening approach using maximum local extrema." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 255. http://dx.doi.org/10.11591/ijece.v9i1.pp255-263.
Full textAbstract:
Mixing or combining different elements for getting enhanced version, is practiced across various areas in real life. Pan-sharpening is a similar technique used in the digital world; a process to combine two images into a fused image that comprises more detailed information. Images referred herein are Panchromatic (PAN) and Multispectral (MS) images. This paper presents a pansharpening algorithm which integrates multispectral and panchromatic images to generate an improved multispectral image. This technique merges the Discrete wavelet transform (WT) and Intensity-Hue-Saturation (IHS) through separate fusing criterion for choosing an approximate and detail sub-images. Whereas the maximal local extrema are used for merging detail sub-images and finally merged high-resolution image is reconstructed through inverse transform of wavelet and IHS. The proposed fusion approach enhances the superiority of the resultant fused image is demonstrated by quality measures like CORR, RMSE, PFE, SSIM, SNR and PSNR with the help of satellite Worldview-II images. The proposed algorithm is correlated with the other fusion techniques through empirical outcomes proves the superiority of the final merged image in terms of resolutions than the others.
15
Yang, Chen, Qingming Zhan, Huimin Liu, and Ruiqi Ma. "An IHS-Based Pan-Sharpening Method for Spectral Fidelity Improvement Using Ripplet Transform and Compressed Sensing." Sensors 18, no. 11 (2018): 3624. http://dx.doi.org/10.3390/s18113624.
Full textAbstract:
Pan-sharpening aims at integrating spectral information from a multi-spectral (MS) image and spatial information from a panchromatic (PAN) image in a fused image with both high spectral and spatial resolutions. Numerous pan-sharpening methods are based on intensity-hue-saturation (IHS) transform, which may cause evident spectral distortion. To address this problem, an IHS-based pan-sharpening method using ripplet transform and compressed sensing is proposed. Firstly, the IHS transform is applied to the MS image to separate intensity components. Secondly, discrete ripplet transform (DRT) is implemented on the intensity component and the PAN image to obtain multi-scale sub-images. High-frequency sub-images are fused by a local variance algorithm and, for low-frequency sub-images, compressed sensing is introduced for the reconstruction of the intensity component so as to integrate the local information from both the intensity component and the PAN image. The specific fusion rule is defined by local difference. Finally, the inverse ripplet transform and inverse IHS transform are coupled to generate the pan-sharpened image. The proposed method is compared with five state-of-the-art pan-sharpening methods and also the Gram-Schmidt (GS) method through visual and quantitative analysis of WorldView-2, Pleiades and Triplesat datasets. The experimental results reveal that the proposed method achieves relatively higher spatial resolution and more desirable spectral fidelity.
16
Li, Wei, Sen Li, Yeheng Wang, and Junying Yun. "Study on Personnel Detection Based on Retinex and YOLOv4 in Building Fire." Journal of Physics: Conference Series 2185, no. 1 (2022): 012039. http://dx.doi.org/10.1088/1742-6596/2185/1/012039.
Full textAbstract:
Abstract When a fire occurs in a building, the internal environment is full of dense smoke, which will greatly hinder the evacuation and rescue of the trapped persons. If the evacuation and rescue are not in time, the life safety of the trapped persons will be seriously threatened. In response to this problem, this paper proposes a method for quickly detecting trapped persons in building fires. This method uses a combination of multi-scale Retinex image sharpening algorithm and YOLOv4 person detection algorithm. First obtain the image information of the fire scene, use the multi-scale Retinex algorithm based on the Gaussian pyramid to perform the sharpening process, and then use the YOLOv4 model to perform the personnel detection on the sharpened fire scene image. The experimental results show that the confidence of image person detection after Retinex sharpening processing has been significantly improved.
17
Dadras Javan, F., F. S. Mortazavi, F. Moradi, and A. Toosi. "NEW HYBRID PAN-SHARPENING METHOD BASED ON TYPE-1 FUZZY-DWT STRATEGY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 247–54. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-247-2019.
Full textAbstract:
Abstract. The purpose of image fusion is to combine two images from the same view in order to produce an image with more information and higher quality. In this paper, a panchromatic image with high spatial resolution and a low-resolution multi-spectral image having rich spectral information are fused together to produce a high-resolution multi-spectral image that heritage the characteristics of both initial images. For this purpose, a hybrid pan-sharpening method, called ‘Improved Fuzzy-DWT’ have been proposed based on the modification of the parameters existed in the latest version of Fuzzy-Wavelet algorithm, and then it was compared with some other algorithms such as PCA, Gram-Schmidt, Wavelet, and two of its hybrid derivatives called PCA-Wavelet and IHS-wavelet. The comparison was conducted using DIV, SSIM, SID, CC, DS, and QNR spectral and spatial quality assessment metrics. The obtained results demonstrate that the proposed hybrid algorithm has relatively better performance in comparison with the other mentioned pan-sharpening techniques in terms of both spectral and spatial qualities, regarding it was superior in terms of SID, DIV, SSIM, DS. From the computational cost standpoint, the proposed method has the most running time compared with the other methods.
18
Goel, Anamika, Jawed Wasim, and Prabhat Kumar Srivastava. "A Noise reduction in the medical images using hybrid combination of filters with nature-inspired Black Widow Optimization Algorithm." International Journal of Experimental Research and Review 30 (April 30, 2023): 433–41. http://dx.doi.org/10.52756/ijerr.2023.v30.040.
Full textAbstract:
This paper proposes an image filtering method to remove the noises in medical images in a controlled manner. To achieve this goal, the optimal parameters of the conventional filters are determined using the nature-inspired black widow (BWO) optimization algorithm to remove the noise efficiently. The BWO algorithm is chosen over other optimization algorithms because it quickly explores the optimal parameter values due to its procreate and cannibalism steps. The procreate step explores new solutions, whereas the cannibalism steps remove the inappropriate solutions while exploring the optimal solution. In the proposed method, speckle and sharpening filters are considered. In the proposed method, initially, medical images are read. After that, they are enhanced using the power law method because images are either low or high contrast. In the power law method, the gamma value plays an important role. Therefore, the optimal gamma value is determined using the BWO algorithm as done for the filter values. After that, noise addition is performed on them and removed them using the speckle filter. Further, the edges of the image are filtered using the sharpening filter. The proposed method is validated on the standard dataset images downloaded from Kaggle. It is found that the proposed method enhances the image and removes the noise in a controlled manner. Besides that, it achieves better Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) in the output.
19
Gui, Zhiguo, and Yi Liu. "An image sharpening algorithm based on fuzzy logic." Optik 122, no. 8 (2011): 697–702. http://dx.doi.org/10.1016/j.ijleo.2010.05.010.
Full text
20
Skoneczny, S. "Morphological sharpening of color images." Bulletin of the Polish Academy of Sciences Technical Sciences 64, no. 1 (2016): 103–13. http://dx.doi.org/10.1515/bpasts-2016-0012.
Full textAbstract:
Abstract This paper presents a novel approach to morphological contrast sharpening of image using the multilevel toggle operator. The concept presented here is a generalization of toggle based contrast operator for gray-level images. The multilevel toggle operator is used to enhance the contrast of multivalued images. In order to perform necessary morphological operations the modified pairwise ordering (MPO) algorithm is proposed. It gives the total order of color pixels. For comparison four other ordering methods are used. The main advantage of the proposed sharpener is its significant contrast enhancing ability when using MPO. Theoretical considerations as well as practical results are shown. Experimental results show its applicability to low-contrast color images.
21
Huo, Bing Quan, and Feng Ling Yin. "A Contour Tracing Algorithm for Ginseng Shape." Applied Mechanics and Materials 401-403 (September 2013): 1268–71. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1268.
Full textAbstract:
More and more applications of computer technology are used in the field of agriculture. In this paper, Image processing technology is applied to ginseng shape. Get a color image from the device, remove the color information and reserve the boundary information. Highlight the image edge information by gradient sharpening, binary image and use 8-neighbourhood algorithm for tracking the border.
22
Niazi, S., M. Mokhtarzade, and F. Saeedzadeh. "A NOVEL IHS-GA FUSION METHOD BASED ON ENHANCEMENT VEGETATED AREA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 543–48. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-543-2015.
Full textAbstract:
Pan sharpening methods aim to produce a more informative image containing the positive aspects of both source images. However, the pan sharpening process usually introduces some spectral and spatial distortions in the resulting fused image. The amount of these distortions varies highly depending on the pan sharpening technique as well as the type of data. Among the existing pan sharpening methods, the Intensity-Hue-Saturation (IHS) technique is the most widely used for its efficiency and high spatial resolution. When the IHS method is used for IKONOS or QuickBird imagery, there is a significant color distortion which is mainly due to the wavelengths range of the panchromatic image. Regarding the fact that in the green vegetated regions panchromatic gray values are much larger than the gray values of intensity image. A novel method is proposed which spatially adjusts the intensity image in vegetated areas. To do so the normalized difference vegetation index (NDVI) is used to identify vegetation areas where the green band is enhanced according to the red and NIR bands. In this way an intensity image is obtained in which the gray values are comparable to the panchromatic image. Beside the genetic optimization algorithm is used to find the optimum weight parameters in order to gain the best intensity image. Visual and statistical analysis proved the efficiency of the proposed method as it significantly improved the fusion quality in comparison to conventional IHS technique. The accuracy of the proposed pan sharpening technique was also evaluated in terms of different spatial and spectral metrics. In this study, 7 metrics (Correlation Coefficient, ERGAS, RASE, RMSE, SAM, SID and Spatial Coefficient) have been used in order to determine the quality of the pan-sharpened images. Experiments were conducted on two different data sets obtained by two different imaging sensors, IKONOS and QuickBird. The result of this showed that the evaluation metrics are more promising for our fused image in comparison to other pan sharpening methods.
23
Putri, Erlinda Ratnasari, Amar Vijai Nasrulloh, and Arfan Eko Fahrudin. "Coloring of Cervical Cancer’s Ct Images to Localize Cervical Cancer." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 2 (2015): 304. http://dx.doi.org/10.11591/ijece.v5i2.pp304-310.
Full textAbstract:
<p>Cervical cancer is the most common gynecologic cancer in women. Cervical cancer and the normal cervix usually have similar attenuations on CT images which are obtained. The normal cervix and the tumour cannot be distinguished on normal CT images. CT image of cervical cancer is used by the experts for the analysis of diseases. In this research study, CT image of cervical cancer is done with process of image segmentation and coloring. The process of image segmentation is done after the image sharpening process and the determination of cervical cancer’s area. Fuzzy C-Means is used as the algorithm for image segmentation. The colors of image segmentation result are changed by program module. The result is the colors of image segmentation uniform with the other results. The image is overlayed with image result of image sharpening process. Coloring image purposes are to distinguish between cervical cancer’s area and normal organ and to localize the existence of cervical cancer. Based on the doctor’s observation, the empirical rate of testing 20 samples on the program is 100%.</p>
24
Bhargavi, S., B. Sadhvik Reddy, T. Sumanth Reddy, T. Sushma, S. Narendra Reddy, and P. Sai Kusuma. "Detection of Illegal Goods using X-ray Image Enhancement Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 1522–32. http://dx.doi.org/10.22214/ijraset.2024.60081.
Full textAbstract:
bstract: An X-ray image enhancement technique integrating USM+CLAHE+HAZEREMOVAL and YOLOV2 for object detection is presented to address the problem of colour distortion in CLAHE enhanced airport security X-ray images. Calculating the grayscale images on the R, G, and B channels of the X-ray image and applying CLAHE enhancement to each, then merging the enhanced R, G, and B grayscale images will take place. After that, USM sharpening operation is applied to the CLAHE-enhanced X-ray image, and then it is merged with the original and USM-sharpened images according to the weight. Later haze removal technique is added to obtained results. For detection, YOLOV2 is used. The results of the experiments reveal that the USM+CLAHE+ HAZEREMOVAL algorithm can successfully improve the security X-ray image while also suppressing colour distortion in the enhanced image
25
Shao Mingsheng, 邵明省, and 李伟 Li Wei. "Image Sharpening Research Based on Improved Quantum Genetic Algorithm." Laser & Optoelectronics Progress 49, no. 1 (2012): 011101. http://dx.doi.org/10.3788/lop49.011101.
Full text
26
Gui, Z. G., P. C. Zhang, J. H. Zhang, and Y. J. Zeng. "A X-ray image sharpening algorithm using nonlinear module." Imaging Science Journal 60, no. 1 (2012): 3–8. http://dx.doi.org/10.1179/174313111x12960531524758.
Full text
27
Yu, Xiaoyan, Chengyou Wang, and Xiao Zhou. "A Robust Color Image Watermarking Algorithm Based on APDCBT and SSVD." Symmetry 11, no. 10 (2019): 1227. http://dx.doi.org/10.3390/sym11101227.
Full textAbstract:
With the wide application of color images, watermarking for the copyright protection of color images has become a research hotspot. In this paper, a robust color image watermarking algorithm based on all phase discrete cosine biorthogonal transform (APDCBT) and shuffled singular value decomposition (SSVD) is proposed. The host image is transformed by the 8 × 8 APDCBT to obtain the direct current (DC) coefficient matrix, and then, the singular value decomposition (SVD) is performed on the DC matrix to embed the watermark. The SSVD and Fibonacci transform are mainly used at the watermark preprocessing stage to improve the security and robustness of the algorithm. The watermarks are color images, and a color quick response (QR) code with error correction mechanism is introduced to be a watermark to further improve the robustness. The watermark embedding and extraction processes are symmetrical. The experimental results show that the algorithm can effectively resist common image processing attacks, such as JPEG compression, Gaussian noise, salt and pepper noise, average filter, median filter, Gaussian filter, sharpening, scaling attacks, and a certain degree of rotation attacks. Compared with the color image watermarking algorithms considered in this paper, the proposed algorithm has better performance in robustness and imperceptibility.
28
C., Ganesh, Satheeshkumar A., Vignesh A., and Thamaraikannan S. "A NOVEL APPROACH FOR FUSION OF LOW RESOLUTION MULTISPECTRAL IMAGES IN REMOTE SENSING." International Journal of Advanced Trends in Engineering and Technology 1, no. 2 (2017): 1–5. https://doi.org/10.5281/zenodo.345429.
Full textAbstract:
Nonlinear decomposition methods comprise a choice to conventional approaches for facing the problem of data fusion. In this paper, we discuss the application of this methodology to a admired remote sensing area called pan sharpening, which consists in the fusion of a low resolution multispectral image and a high-resolution panchromatic image. We design a complete pan sharpening scheme based on the use of morphological half gradient operators and demonstrate the application of this algorithm through the comparison with the state-of-the-art approaches. Four data sets obtained by the different satellites are employed for the performance monitoring, testifying the effectiveness of the proposed method in producing top-class images with a setting independent of the specific sensor.
29
Li, Ronghua, Qingqing Chu, Kaichun Zhao, and Junfeng Piao. "Foggy image–sharpening method with multi-channel polarization information system." Advances in Mechanical Engineering 11, no. 3 (2019): 168781401983414. http://dx.doi.org/10.1177/1687814019834147.
Full textAbstract:
This article aims to reduce the influence of heavy fog on the outdoor imaging equipment and to maximally improve the foggy image resolution. It is known that reflected light of the object includes the abundant target polarization information. The foggy image can be restored by these information of the target and the airlight. Therefore, this article introduces a multi-channel polarization information system and defogging algorithm accordingly. The polarization information system provides a necessary solution for the accurate application of a specific algorithm, since it can ensure the accuracy of acquired image information. The key point of the proposed algorithm lies in accurately estimating the parameters in the polarization defogging model. Based on the normal threshold distribution comparison, it can avoid the highlight portion of the non-sky area in the image and accurately estimate the airlight intensity at infinite distance so as to effectively reduce the color distortion of the bright area. The median filtering algorithm is used to obtain the airlight degree of polarization by using three obtained polarization scenes. At last, this article analyzes the experimental results through defogging evaluation indexes and compared the result obtained by this algorithm with others.
30
Gao, Yin, Li Jun Yun, Jun Sheng Shi, and Fei Yan Cheng. "Enhancement Algorithm of Color Fog Image Based on the Adaptive Scale and S-Cosine Curve." Applied Mechanics and Materials 513-517 (February 2014): 3362–67. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3362.
Full textAbstract:
To deal with the image contrast and color fidelity details problem in the traditional Center around the Retinex image enhancement algorithms, Enhancement algorithm of color fog image based on the adaptive scale and s-cosine curve is proposed. Firstly, the image is transformed into the RGB color space. Then the each channel pixel values can be stretched the grayscale range by S-cosine curve and introduces the local correction function. It can calculate the scale of the Gaussian kernel, and then proceeds to do the Gamma correction for the estimates of the reflection component, obtains the multi-scale image by the weighted average. Afterwards, the obtained image is used to global nonlinear correction, image sharpening and smoothing, and being superimposed reflection components, achieving the image enhancement. At last, it can carry on the intensity adjustment and grayscale adjustment for the obtained image. Through the subjective observation and objective evaluation, this algorithm is better than the traditional center around Retinex algorithm and MSRCR algorithm in processing effect.
31
MISS., DESHPANDE MAYURI, and KOLKURE V.S. MR. "ADVANCED FILTERING TECHNIQUES IN IMAGE PROCESSING : A SURVEY." JournalNX - A Multidisciplinary Peer Reviewed Journal 3, no. 3 (2017): 148–50. https://doi.org/10.5281/zenodo.1463745.
Full textAbstract:
Nowadays, in the field of image processing so many new technologies are found out and for various purposes such as filtering, edge smoothing, edge sharpening, zooming, enhancing, fusing, noise and blur removing . But these primitive and conventional methods bring some noises and halo artifacts with them as well as they need more time for processing. The algorithms used in these methods are very large and complicated. This method mainly focuses on filtering of images along with image enhancing, image fusion, haze removal and detail enhancement. Weighted Guided Image Filter uses two types of filtering algorithm: First is Global Filtering and second is Local Filtering. Therefore, it makes filtering process easy, less complicated and less time consuming. https://journalnx.com/journal-article/20150218
32
Xu, Hongyan. "Digital media zero watermark copyright protection algorithm based on embedded intelligent edge computing detection." Mathematical Biosciences and Engineering 18, no. 5 (2021): 6771–89. http://dx.doi.org/10.3934/mbe.2021336.
Full textAbstract:
<abstract> <p>With the rapid development of computer technology and network communication technology, copyright protection caused by widely spread digital media has become the focus of attention in various fields. For digital media watermarking technology research emerge in endlessly, but the results are not ideal. In order to better realize the copyright identification and protection, based on the embedded intelligent edge computing detection technology, this paper studies the zero watermark copyright protection algorithm of digital media. Firstly, this paper designs an embedded intelligent edge detection module based on Sobel operator, including image line buffer module, convolution calculation module and threshold processing module. Then, based on the embedded intelligent edge detection module, the Arnold transform of image scrambling technology is used to preprocess the watermark, and finally a zero watermark copyright protection algorithm is constructed. At the same time, the robustness of the proposed algorithm is tested. The image is subjected to different proportion of clipping and scaling attacks, different types of noise, sharpening and blur attacks, and the detection rate and signal-to-noise ratio of each algorithm are calculated respectively. The performance of the watermark image processed by this algorithm is evaluated subjectively and objectively. Experimental data show that the detection rate of our algorithm is the highest, which is 0.89. In scaling attack, the performance of our algorithm is slightly lower than that of Fourier transform domain algorithm, but it is better than the other two algorithms. The Signal to Noise Ratio of the algorithm is 36.854% in P6 multiplicative noise attack, 39.638% in P8 sharpening edge attack and 41.285% in fuzzy attack. This shows that the algorithm is robust to conventional attacks. The subjective evaluation of 33% and 39% of the images is 5 and 4. The mean values of signal to noise ratio, peak signal to noise ratio, mean square error and mean absolute difference are 20.56, 25.13, 37.03 and 27.64, respectively. This shows that the watermark image processed by this algorithm has high quality. Therefore, the digital media zero watermark copyright protection algorithm based on embedded intelligent edge computing detection is more robust, and its watermark invisibility is also very superior, which is worth promoting.</p> </abstract>
33
Bhargavi, S., B. Sadvik Reddy, T. Sumanth Reddy, T. Sushma, S. Narendra Reddy, and P. Sai Kusuma. "Detection of Illegal Goods using X-ray Image Enhancement Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 572–78. http://dx.doi.org/10.22214/ijraset.2024.58367.
Full textAbstract:
Abstract: In order to facilitate the security personnel to identify the items in the X-ray image, it is necessary to enhance the security X-ray image to highlight the color, edge, shape and other details of the different items in the image, so that the security personnel can identify the inspected items more accurately and quickly, to ensure the safe operation of the airport. Aiming at the problem of color distortion in CLAHE enhanced airport security X-ray images, an X-ray image enhancement algorithm combining USM+CLAHE is proposed. In this work, First, we calculate the grayscale images on the R, G, and B channels of the X-ray image and perform Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement respectively, and then merge the enhanced R, G, and B grayscale images. Next, we perform the Unsharp Mask (USM) sharpening operation on the Xray image enhanced by CLAHE, and finally merge the original image and the USM sharpened image according to the weight. The experimental results show that the USM+CLAHE algorithm can effectively enhance the security X-ray image, and at the same time can suppress the color distortion of the enhanced image.
34
Zhao, Wen Dong, Hui Qi, and Hai Yan Zhou. "Segmentation Algorithm of Traffic Prohibited Area Based on Wavelet." Advanced Materials Research 542-543 (June 2012): 1316–19. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1316.
Full textAbstract:
The status of transportation industry development of China has shown the necessity and urgency of the development of intelligent transportation systems. This article proposed a segmentation algorithm of traffic prohibited region based on wavelet transform. Based on the de-noising and image enhancement, sharpening pretreatment of the traffic video image captured on real-time, the algorithm combines the method determining the quadrilateral based on the sample images manually with the image segmentation based on wavelet transform in order to get the segmentation of traffic prohibited region which will be used in the detection of vehicle pressing highway central line region. The experimental results show that in the algorithm not only meet the real-time requirement of foundations but also improves the successful rate of detection results.
35
Chen, Yingxia, and Guixu Zhang. "A Pan-Sharpening Method Based on Evolutionary Optimization and IHS Transformation." Mathematical Problems in Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/8269078.
Full textAbstract:
In many remote sensing applications, users usually prefer a multispectral image with both high spectral and high spatial information. This high quality image could be obtained by pan-sharpening techniques which fuse a high resolution panchromatic (PAN) image and a low resolution multispectral (MS) image. In this paper, we propose a new technique to do so based on the adaptive intensity-hue-saturation (IHS) transformation model and evolutionary optimization. The basic idea is to reconstruct the target image through a parameterized adaptive IHS transformation. An optimization objective is thus introduced by considering the relations between the fused image and the original PAN and MS images. The control parameters are optimized by an evolutionary algorithm. Experimental results show that our new approach is practical and performs much better than some state-of-the-art techniques according to the performance metrics.
36
Sirait, Pahala, Irpan Adiputra Pardosi, Apriyanto Halim, and Tommy -. "Implementasi Kombinasi Metode AFF dan FBET Untuk Peningkatan Kualitas Citra." Jurnal SIFO Mikroskil 20, no. 1 (2019): 1–10. http://dx.doi.org/10.55601/jsm.v20i1.550.
Full textAbstract:
Image enhancement is one of the most popular research topics currently in the field of image processing. Often image has poor quality caused by various factors such as lighting factor, enviromental factor or low quality of camera, and others. A number of these disturbances often become a barrier in improving the image quality where handling is the main objective of this research is done in the form of methodology base on the combination of AFF dan FBET algorithm. The result of the test on the proposed methodology show that in the process of noise loss continued with the image sharpening process obtained the value of PSNR = 18.56 dB is more optimal than the first done image sharpening process with the value of PSNR = 18.10 dB.
37
Sihotang, Amran Sihotang, and Petti Indrayati. "3D Image Side Sharpening Using Fourier Phase Only Synthetis Method." Jurnal Info Sains : Informatika dan Sains 10, no. 2 (2020): 24–29. http://dx.doi.org/10.54209/infosains.v10i2.34.
Full textAbstract:
In the process of the Fourier Phase Only Synthetis method on two images, the observer must get the impression that the imagery actually changes shape to an intermediate form before it changes to the destination image. These changes must occur in a regular and consistent manner to achieve the image of the goal. This sharpening system is one of the systems that aims for this form change process is widely used in applications in the field of entertainment, computer animation, scientific visualization and education. The sharpening system on the 3-dimensional side of the image aims to identify the pattern of the image. Good image quality if it has good contrast and can describe clear ridges and valleys structures. Based on previous research that the study was conducted improvements with Fourier Phase Only Synthetis where the algorithm used simultaneously estimates all the intrinsic properties of. The quality of image sharpening relates to the clarity of ridge structure on the image side. A good image will have a good contrast and will well depict ridges and valleys, if the fingerprint imagery is of poor quality then it will have less contrast so it will less clearly describe the boundaries of ridges (hills). From the implementation of Fourier Phase Only Synthetis Analysis, using the main parameters ridge orientation image, has been successfully obtained the results of image side improvement well. This image side improvement will greatly help to improve the quality of 3-dimensional image extraction, by specifying constant values to get the bestresults.
38
Zhu, Xiao Xiang, and Richard Bamler. "A Sparse Image Fusion Algorithm With Application to Pan-Sharpening." IEEE Transactions on Geoscience and Remote Sensing 51, no. 5 (2013): 2827–36. http://dx.doi.org/10.1109/tgrs.2012.2213604.
Full text
39
Guo Rui, 郭瑞, 党建武 Dang Jianwu, 沈瑜 Shen Yu, and 刘成 Liu Cheng. "Foggy Image Sharpening Algorithm Based on Multi-Scale Geometric Analysis." Laser & Optoelectronics Progress 55, no. 11 (2018): 111009. http://dx.doi.org/10.3788/lop55.111009.
Full text
40
Ma, Tinghuai, Lu Li, Sai Ji, et al. "Optimized Laplacian image sharpening algorithm based on graphic processing unit." Physica A: Statistical Mechanics and its Applications 416 (December 2014): 400–410. http://dx.doi.org/10.1016/j.physa.2014.09.026.
Full text
41
Gao, Yin, Li Jun Yun, Jun Sheng Shi, and Fei Yan Cheng. "Enhancement Algorithm of Color Fog Image Based on the Adaptive Scale and Multi-Parameter Correction." Advanced Materials Research 989-994 (July 2014): 3838–43. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3838.
Full textAbstract:
To deal with the image contrast and color fidelity details problem in the traditional Center around the Retinex image enhancement algorithms, Enhancement Algorithm of Color Fog Image Based on the Adaptive Scale and Multi-parameter correction is proposed. First, the saturation component of the HSI color space is applied to the nonlinear correction. Then according to the brightness of the image pixel values, it can calculates the scale of the Gaussian kernel, and then proceeds to do the multi-parameter correction for the estimates of the reflection component, obtains the multi-scale image by the weighted average. At last, the obtained image is used to adjust the display correction, image sharpening and smoothing, and being superimposed reflection components, achieving the image enhancement. Through the subjective observation and objective evaluation, this algorithm is better than the traditional center around Retinex algorithm in treatment effect, and also saves a large amount of processing time.
42
Liu, Wencheng, Zhen Chen, Zhiyu Jiang, et al. "Ground-Moving Target Relocation for a Lightweight Unmanned Aerial Vehicle-Borne Radar System Based on Doppler Beam Sharpening Image Registration." Electronics 14, no. 9 (2025): 1760. https://doi.org/10.3390/electronics14091760.
Full textAbstract:
With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground-moving target indication (GMTI) radar systems has received great interest. However, because of size, weight, and power (SWaP) limitations, the UAV may not be able to equip a highly accurate inertial navigation system (INS), which leads to reduced accuracy in the moving target relocation. To solve this issue, we propose using an image registration algorithm, which matches a Doppler beam sharpening (DBS) image of detected moving targets to a synthetic aperture radar (SAR) image containing coordinate information. However, when using conventional SAR image registration algorithms such as the SAR scale-invariant feature transform (SIFT) algorithm, additional difficulties arise. To overcome these difficulties, we developed a new image-matching algorithm, which first estimates the errors of the UAV platform to compensate for geometric distortions in the DBS image. In addition, to showcase the relocation improvement achieved with the new algorithm, we compared it with the affine transformation and second-order polynomial algorithms. The findings of simulated and real-world experiments demonstrate that our proposed image transformation method offers better moving target relocation results under low-accuracy INS conditions.
43
Zhu, Dachang. "Underwater Image Enhancement Based on the Improved Algorithm of Dark Channel." Mathematics 11, no. 6 (2023): 1382. http://dx.doi.org/10.3390/math11061382.
Full textAbstract:
Enhancing underwater images presents a challenging problem owing to the influence of ocean currents, the refraction, absorption and scattering of light by suspended particles, and the weak illumination intensity. Recently, different methods have relied on the underwater image formation model and deep learning techniques to restore underwater images. However, they tend to degrade the underwater images, interfere with background clutter and miss the boundary details of blue regions. An improved image fusion and enhancement algorithm based on a prior dark channel is proposed in this paper based on graph theory. Image edge feature sharpening, and dark detail enhancement by homomorphism filtering in CIELab colour space are realized. In the RGB colour space, the multi-scale retinal with colour restoration (MSRCR) algorithm is used to improve colour deviation and enhance colour saturation. The contrast-limited adaptive histogram equalization (CLAHE) algorithm defogs and enhances image contrast. Finally, according to the dark channel images of the three processing results, the final enhanced image is obtained by the linear fusion of multiple images and channels. Experimental results demonstrate the effectiveness and practicality of the proposed method on various data sets.
44
Tang, Qing-Ju, Juan Ji, Wei-Ming Fan, et al. "Experimental study on infrared phase-locked thermal imaging inspection of carbon fiber reinforced polymer laminates." Thermal Science 26, no. 2 Part A (2022): 1105–11. http://dx.doi.org/10.2298/tsci2202105t.
Full textAbstract:
Aiming at the debonding defect of carbon fiber reinforced polymer laminates, an infrared phase-locked thermal imaging inspection system was established, and the influence of different defect diameter and depth parameters on the test was analyzed. The principal component analysis algorithm and Karhunen-Loeve Transform algorithm are used to process the image sequence, and the signal-to-noise ratio is calculated. It is concluded that principal component analysis algorithm can improve the image quality more. Gray enhancement and sharpening filter are used to improve the image clarity, thus accurately segmenting the defect features, and realize a clear and intuitive visual image.
45
Robin, Hojiwala. "Image Color Balance with Laplacian and Gaussian Pyramid (CBLGP) Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29427.
Full textAbstract:
In recent times, technology has rapidly and significantly evolved across all sectors. Digital image processing stands out as a modern technology aimed at achieving clear images. However, digitized images often encounter issues of low quality, such as unclear or underwater images that require enhancement for better visibility. These problems stem from factors like deficient focusing, lighting, and various constraints leading to low contrast, shading, and artifacts. Underwater and satellite images consistently face less-than-ideal conditions due to environmental factors like light refraction in water, particle scattering, and dust in aquatic environments. Similarly, challenges in space, such as poor illumination and lack of contrast, further complicate image analysis. Overcoming these obstacles is crucial for extracting valuable information, necessitating advanced processing techniques. This paper introduces an enhanced Gaussian/Laplace color balance-fusion algorithm designed to improve image visibility. Modifications to certain equations result in sharper and clearer images. The algorithm begins by determining the white balance of the input RGB color image and subsequently enhances its intensity. Edge improvement is carried out separately using a depth filter. The weights for each image are then determined and combined to form a Laplace Pyramid. A color restoration technique is applied to process the resulting image, producing the final enhanced image. While existing methods for image contrast enhancement typically focus on image features, they often neglect user characteristics. This paper explores the application of image sharpening, a prominent image enhancement technique, in clearing underwater or low-quality images using the proposed algorithm. Keywords: EDSHE, High pass filter, White patch ratix, Laplacian pyramid, Color restoration.
46
Maurya, Lalit, Prasant Kumar Mahapatra, and Amod Kumar. "A Fusion of Cuckoo Search and Multiscale Adaptive Smoothing Based Unsharp Masking for Image Enhancement." International Journal of Applied Metaheuristic Computing 10, no. 3 (2019): 151–74. http://dx.doi.org/10.4018/ijamc.2019070108.
Full textAbstract:
Image enhancement means to improve the visual appearance of an image by increasing its contrast and sharpening the features. This article presents a fusion of cuckoo search optimization-based image enhancement (CS-IE) and multiscale adaptive smoothing based unsharping method (MAS-UM) for image enhancement. The fusion strategy is introduced to improve the deficiency of enhanced image that suppresses the saturation and over-sharpness artefacts in order to obtain a visually pleasing result. The ideology behind the selection of fusion images (candidate) is that one image should have high sharpness or contrast with maximum entropy and other should be high Peak Signal-to-Noise Ratio (PSNR) sharp image, to provide a better trade-off between sharpness and noise. In this article, the CS-IE and MAS-UM results are fused to combine their complementary advantages. The proposed algorithms are applied to lathe tool images and some natural standard images to verify their effectiveness. The results are compared with conventional enhancement techniques such as Histogram equalization (HE), Linear contrast stretching (LCS), Contrast-limited adaptive histogram equalization (CLAHE), standard PSO image enhancement (PSO-IE), Differential evolution image enhancement (DE-IE) and Firefly algorithm-based image enhancement (FA-IE) techniques.
47
Luo, Kaiqing, Manling Lin, Pengcheng Wang, Siwei Zhou, Dan Yin, and Haolan Zhang. "Improved ORB-SLAM2 Algorithm Based on Information Entropy and Image Sharpening Adjustment." Mathematical Problems in Engineering 2020 (September 23, 2020): 1–13. http://dx.doi.org/10.1155/2020/4724310.
Full textAbstract:
Simultaneous Localization and Mapping (SLAM) has become a research hotspot in the field of robots in recent years. However, most visual SLAM systems are based on static assumptions which ignored motion effects. If image sequences are not rich in texture information or the camera rotates at a large angle, SLAM system will fail to locate and map. To solve these problems, this paper proposes an improved ORB-SLAM2 algorithm based on information entropy and sharpening processing. The information entropy corresponding to the segmented image block is calculated, and the entropy threshold is determined by the adaptive algorithm of image entropy threshold, and then the image block which is smaller than the information entropy threshold is sharpened. The experimental results show that compared with the ORB-SLAM2 system, the relative trajectory error decreases by 36.1% and the absolute trajectory error decreases by 45.1% compared with ORB-SLAM2. Although these indicators are greatly improved, the processing time is not greatly increased. To some extent, the algorithm solves the problem of system localization and mapping failure caused by camera large angle rotation and insufficient image texture information.
48
T Joy, Manoj, B. Priestly Shan, and Geevarghese Titus. "Gradient-adaptive Nonlinear Sharpening for Dental Radiographs." International journal of electrical and computer engineering systems 14, no. 6 (2023): 685–93. http://dx.doi.org/10.32985/ijeces.14.6.8.
Full textAbstract:
Unsharp Masking is a popular image processing technique used for improving the sharpness of structures on dental radiographs. However, it produces overshoot artefact and intolerably amplifies noise. On radiographs, the overshoot artefact often resembles the indications of prosthesis misfit, pathosis, and pathological features associated with restorations. A noise- robust alternative to the Unsharp Masking algorithm, termed Gradient-adaptive Nonlinear Sharpening (GNS) which is free from overshoot and discontinuity artefacts, is proposed in this paper. In GNS, the product of the arbitrary scalar termed as ‘scale’ and the difference between the output of the Adaptive Edge Smoothing Filter (AESF) and the input image, weighted by the normalized gradient magnitude is added to the input image. AESF is a locally-adaptive 2D Gaussian smoothing kernel whose variance is directly proportional to the local value of the gradient magnitude. The dataset employed in this paper is downloaded from the Mendeley data repository having annotated panoramic dental radiographs of 116 patients. On 116 dental radiographs, the values of Saturation Evaluation Index (SEI), Sharpness of Ridges (SOR), Edge Model Based Contrast Metric (EMBCM), and Visual Information Fidelity (VIF) exhibited by the Unsharp Masking are 0.0048 ± 0.0021, 4.4 × 1013 ± 3.8 × 1013, 0.2634 ± 0.2732 and 0.9898 ± 0.0122. The values of these quality metrics corresponding to the GNS are 0.0042 ± 0.0017, 2.2 × 1013 ± 1.8 × 1013, 0.5224 ± 0.1825, and 1.0094 ± 0.0094. GNS exhibited lower values of SEI and SOR and higher values of EMBCM and VIF, compared to the Unsharp Masking. Lower values of SEI and SOR, respectively indicate that GNS is free from overshoot artefact and saturation and the quality of edges in the output images of GNS is less affected by noise. Higher values of EMBCM and VIF, respectively confirm that GNS is free from haloes as it produces thin and sharp edges and the sharpened images are of good information fidelity.
49
Wang, Yulei, Qingyu Zhu, Yao Shi, Meiping Song, and Chunyan Yu. "A Spatial-Enhanced LSE-SFIM Algorithm for Hyperspectral and Multispectral Images Fusion." Remote Sensing 13, no. 24 (2021): 4967. http://dx.doi.org/10.3390/rs13244967.
Full textAbstract:
The fusion of a hyperspectral image (HSI) and multispectral image (MSI) can significantly improve the ability of ground target recognition and identification. The quality of spatial information and the fidelity of spectral information are normally contradictory. However, these two properties are non-negligible indicators for multi-source remote-sensing images fusion. The smoothing filter-based intensity modulation (SFIM) method is a simple yet effective model for image fusion, which can improve the spatial texture details of the image well, and maintain the spectral characteristics of the image significantly. However, traditional SFIM has a poor effect for edge information sharpening, leading to a bad overall fusion result. In order to obtain better spatial information, a spatial filter-based improved LSE-SFIM algorithm is proposed in this paper. Firstly, the least square estimation (LSE) algorithm is combined with SFIM, which can effectively improve the spatial information quality of the fused image. At the same time, in order to better maintain the spatial information, four spatial filters (mean, median, nearest and bilinear) are used for the simulated MSI image to extract fine spatial information. Six quality indexes are used to compare the performance of different algorithms, and the experimental results demonstrate that the LSE-SFIM based on bilinear (LES-SFIM-B) performs significantly better than the traditional SFIM algorithm and other spatially enhanced LSE-SFIM algorithms proposed in this paper. Furthermore, LSE-SFIM-B could also obtain similar performance compared with three state-of-the-art HSI-MSI fusion algorithms (CNMF, HySure, and FUSE), while the computing time is much shorter.
50
Park, Gyuseok, and Sangmin Lee. "Environmental Noise Classification Using Convolutional Neural Networks with Input Transform for Hearing Aids." International Journal of Environmental Research and Public Health 17, no. 7 (2020): 2270. http://dx.doi.org/10.3390/ijerph17072270.
Full textAbstract:
Hearing aids are essential for people with hearing loss, and noise estimation and classification are some of the most important technologies used in devices. This paper presents an environmental noise classification algorithm for hearing aids that uses convolutional neural networks (CNNs) and image signals transformed from sound signals. The algorithm was developed using the data of ten types of noise acquired from living environments where such noises occur. Spectrogram images transformed from sound data are used as the input of the CNNs after processing of the images by a sharpening mask and median filter. The classification results of the proposed algorithm were compared with those of other noise classification methods. A maximum correct classification accuracy of 99.25% was achieved by the proposed algorithm for a spectrogram time length of 1 s, with the correct classification accuracy decreasing with increasing spectrogram time length up to 8 s. For a spectrogram time length of 8 s and using the sharpening mask and median filter, the classification accuracy was 98.73%, which is comparable with the 98.79% achieved by the conventional method for a time length of 1 s. The proposed hearing aid noise classification algorithm thus offers less computational complexity without compromising on performance.